o1keyandClaude Opus 4.6
40b10209a4
- AsyncImageGenerator → NanoBananaV2 - BatchAsyncImageGenerator → NanoBananaV2Batch - 移除 universal_llm 中的 gpt-5.4 模型 - 移除 models_config 中的 nano-banana-pro-官方 和 nano-banana-2-官方 Co-Authored-By: Claude Opus 4.6 <[email protected]>
831 lines
33 KiB
Python
831 lines
33 KiB
Python
"""
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Nano Banana Pro 节点
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ComfyUI 自定义节点，用于调用 Gemini 模型生成图像
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"""
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import time
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import math
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import random
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import asyncio
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import aiohttp
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from concurrent.futures import ThreadPoolExecutor
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from typing import Optional, Tuple, List
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import torch
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import numpy as np
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from PIL import Image
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from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
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from ..utils.file_utils import ImageInfo, generate_timestamp_filename, save_image
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from ..clients.gemini_client import GeminiAPIClient
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from ..models_config import (
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get_enabled_models, get_model_description,
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get_model_supported_aspect_ratios, get_all_supported_aspect_ratios,
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get_model_supported_resolutions, get_all_supported_resolutions
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)
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# 检查 folder_paths 是否可用
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try:
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import folder_paths
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FOLDER_PATHS_AVAILABLE = True
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except ImportError:
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FOLDER_PATHS_AVAILABLE = False
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# 导入 ComfyUI 原生进度条
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try:
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from comfy.utils import ProgressBar
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PROGRESS_BAR_AVAILABLE = True
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except ImportError:
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PROGRESS_BAR_AVAILABLE = False
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print("⚠️ NanoBananaPro: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
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# 内存监控（可选）
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try:
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import psutil
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MEMORY_MONITOR_AVAILABLE = True
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except ImportError:
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MEMORY_MONITOR_AVAILABLE = False
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print("⚠️ NanoBananaPro: psutil 不可用，内存监控功能禁用")
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# ============================================================================
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# 调试日志配置
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# ============================================================================
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# 是否启用调试日志（打印完整的 API 响应内容）
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# 设置为 True 以启用调试日志，False 以禁用
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DEBUG_LOG_ENABLED = True
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# 是否启用请求体日志（打印发送给 API 的请求体，base64 图片数据将自动截断）
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# 设置为 True 以启用请求体日志，False 以禁用
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REQUEST_LOG_ENABLED = True
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# ============================================================================
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_NODE = "Nano Banana Pro"
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def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
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"""
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将 PIL Image 列表转换为 ComfyUI tensor，安全处理多张不同尺寸的情况。
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策略：
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- 以像素数最大的图尺寸为基准
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- 只输出与最大尺寸相同的图，其余较小的图丢弃
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"""
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if not images:
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placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
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return pil_to_tensor([placeholder])
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base_size = max(images, key=lambda img: img.size[0] * img.size[1]).size
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matched = [img for img in images if img.size == base_size]
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skipped = [img for img in images if img.size != base_size]
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if skipped:
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sizes_str = ", ".join(f"{img.size[0]}×{img.size[1]}" for img in skipped)
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print(
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f"{node_label}: 丢弃 {len(skipped)} 张较小尺寸的图 ({sizes_str})，"
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f"仅输出最大尺寸 {base_size[0]}×{base_size[1]} 的 {len(matched)} 张"
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)
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return pil_to_tensor(matched)
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class NanoBananaPro:
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"""
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Nano Banana Pro 节点
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功能：
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- 文生图：基于提示词生成图像
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- 图生图：基于输入图像和提示词生成新图像
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- 批量生成：支持并发生成多张图像
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注意：
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- 支持的模型列表从 models_config.py 动态加载
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- 要添加/禁用模型，请编辑 models_config.py 文件
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"""
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# 支持的模型列表（从配置文件动态加载）
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MODELS = None # 将在 INPUT_TYPES 中动态获取
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# 支持的宽高比列表（全量：所有启用模型的并集，动态加载）
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# 实际渲染时通过 get_all_supported_aspect_ratios() 获取
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ASPECT_RATIOS = [
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"1:1", "4:3", "3:4", "16:9", "9:16",
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"2:3", "3:2", "4:5", "5:4", "21:9",
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"1:4", "4:1", "1:8", "8:1"
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]
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# 支持的分辨率列表（全量兜底，实际由 get_all_supported_resolutions() 动态生成）
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RESOLUTIONS = ["512px", "1K", "2K", "4K"]
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def __init__(self):
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"""初始化节点"""
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self.client = None
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@classmethod
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def INPUT_TYPES(cls):
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"""
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定义输入参数
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ComfyUI 节点规范：
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- required: 必选参数
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- optional: 可选参数
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"""
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# 从配置文件动态获取启用的模型列表
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enabled_models = get_enabled_models()
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# 如果没有启用的模型，使用空列表（会导致节点不可用，提示用户配置）
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if not enabled_models:
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enabled_models = ["请在 models_config.py 中启用至少一个模型"]
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# 动态获取所有启用模型支持的宽高比（去重合并）
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all_aspect_ratios = get_all_supported_aspect_ratios()
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if not all_aspect_ratios:
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all_aspect_ratios = cls.ASPECT_RATIOS
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# 动态获取所有启用模型支持的分辨率（去重合并）
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all_resolutions = get_all_supported_resolutions()
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if not all_resolutions:
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all_resolutions = cls.RESOLUTIONS
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# 创建9个独立的图像输入
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optional_inputs = {}
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for i in range(1, 10): # 1-9
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optional_inputs[f"参考图{i}"] = ("IMAGE",)
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optional_inputs["代理端口（如7897）"] = ("STRING", {
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"default": "",
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"multiline": False,
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"placeholder": "本地代理端口，如 7897（Clash Verge）或 10808（v2rayN），留空不使用"
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})
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return {
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"required": {
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"prompt": ("STRING", {
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"default": "一个中国女子的OOTD",
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"multiline": True
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}),
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"模型": (enabled_models, {
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"default": enabled_models[0]
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}),
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"宽高比": (all_aspect_ratios, {
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"default": "1:1"
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}),
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"分辨率": (all_resolutions, {
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"default": "2K"
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}),
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"生图数量": ("INT", {
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"default": 1,
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"min": 1,
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"max": 1000,
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"step": 1
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}),
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"谷歌搜索（联网）": (["关闭", "打开"], {
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"default": "关闭"
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}),
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"图片搜索（联网）": (["关闭", "打开"], {
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"default": "关闭"
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}),
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"返回格式": (["url", "base64"], {
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"default": "url"
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 0xffffffffffffffff
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})
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},
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"optional": optional_inputs
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}
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# 返回值类型
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("输出图像",)
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# 导入 ComfyUI 的文件夹路径管理
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try:
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import folder_paths
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FOLDER_PATHS_AVAILABLE = True
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except ImportError:
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FOLDER_PATHS_AVAILABLE = False
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# 执行函数名
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FUNCTION = "generate"
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# 节点分类
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CATEGORY = "image/generation"
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def resize_to_megapixels(
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self,
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image: Image.Image,
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target_megapixels: float
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) -> Image.Image:
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"""
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将图像缩放到指定的总像素数，保持纵横比
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Args:
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image: PIL Image 对象
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target_megapixels: 目标像素数（百万像素）
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Returns:
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缩放后的 PIL Image
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Example:
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>>> resized = self.resize_to_megapixels(img, 2.0) # 缩放到2百万像素
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"""
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# 计算当前像素数
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current_pixels = image.width * image.height
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target_pixels = int(target_megapixels * 1_000_000)
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# 如果当前像素数已经接近目标，则不缩放
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if abs(current_pixels - target_pixels) / target_pixels < 0.05:
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return image
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# 计算缩放比例
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scale = (target_pixels / current_pixels) ** 0.5
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# 计算新尺寸
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new_width = int(image.width * scale)
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new_height = int(image.height * scale)
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# 确保至少为1像素
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new_width = max(1, new_width)
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new_height = max(1, new_height)
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# 使用 Lanczos 重采样
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resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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return resized_image
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def validate_inputs(
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self,
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images: Optional[torch.Tensor],
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batch_size: int
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) -> None:
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"""
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验证输入参数
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Args:
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images: 输入图像张量（可选）
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batch_size: 批次大小
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Raises:
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ValueError: 如果输入参数不合法
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"""
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# 检查图像数量
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if images is not None:
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num_images = images.shape[0]
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if num_images > 14:
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raise ValueError(
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f"输入图像数量 {num_images} 超过限制 14 张，请减少输入图像数量"
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)
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# 检查批次大小
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if batch_size < 1 or batch_size > 1000:
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raise ValueError(
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f"批次大小 {batch_size} 超出范围 [1, 1000]"
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)
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async def _generate_single_task(
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self,
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session: aiohttp.ClientSession,
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prompt: str,
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model: str,
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resolution: str,
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aspect_ratio: str,
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images: List[Image.Image],
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output_folder: str,
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global_task_index: int,
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enable_grounding: bool = False,
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enable_image_search: bool = False,
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save_to_disk: bool = True,
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image_format: str = "url",
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) -> dict:
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"""执行单个生成任务"""
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result = {
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"global_task_index": global_task_index,
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"prompt": prompt,
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"success": False,
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"generated_count": 0,
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"saved_files": [],
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"output_images": [],
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"error": None
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}
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try:
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gen_result = await self.client.generate_single_async(
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prompt=prompt,
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model=model,
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resolution=resolution,
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aspect_ratio=aspect_ratio,
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images=images if images else None,
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session=session,
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debug=DEBUG_LOG_ENABLED,
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debug_request=REQUEST_LOG_ENABLED,
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enable_grounding=enable_grounding,
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enable_image_search=enable_image_search,
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image_format=image_format,
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)
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if gen_result:
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images_list, _ = gen_result
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if save_to_disk:
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for gen_img in images_list:
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output_path = generate_timestamp_filename(
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output_folder=output_folder,
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extension=".png"
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)
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save_image(gen_img, output_path)
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result["saved_files"].append(output_path)
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gen_img = None
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else:
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result["output_images"] = images_list
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result["success"] = True
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result["generated_count"] = len(images_list)
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except Exception as e:
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result["error"] = str(e)
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return result
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async def _process_batch_async(
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self,
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prompts: List[str],
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model: str,
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resolution: str,
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aspect_ratio: str,
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images_per_prompt: int,
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input_images: List[Image.Image],
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output_folder: str,
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pbar=None,
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||
enable_grounding: bool = False,
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enable_image_search: bool = False,
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save_to_disk: bool = True,
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image_format: str = "url",
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) -> List[dict]:
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"""异步批量处理：每个提示词独立调用 API"""
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# 构建任务列表：(prompt, sub_index) 用于 images_per_prompt > 1 的情况
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tasks_def = []
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for p_idx, prompt in enumerate(prompts):
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for sub_idx in range(images_per_prompt):
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tasks_def.append((p_idx, sub_idx, prompt))
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total_tasks = len(tasks_def)
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num_prompts = len(prompts)
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max_concurrent = 50
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num_batches = math.ceil(total_tasks / max_concurrent)
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||
all_results = []
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completed = 0
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success_count = 0
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fail_count = 0
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||
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||
connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0)
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||
async with aiohttp.ClientSession(connector=connector) as session:
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for batch_idx in range(num_batches):
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start_idx = batch_idx * max_concurrent
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end_idx = min(start_idx + max_concurrent, total_tasks)
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||
|
||
tasks = []
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||
for i in range(start_idx, end_idx):
|
||
_, _, prompt = tasks_def[i]
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||
task = asyncio.create_task(
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self._generate_single_task(
|
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session=session,
|
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prompt=prompt,
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model=model,
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||
resolution=resolution,
|
||
aspect_ratio=aspect_ratio,
|
||
images=input_images,
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||
output_folder=output_folder,
|
||
global_task_index=i,
|
||
enable_grounding=enable_grounding,
|
||
enable_image_search=enable_image_search,
|
||
save_to_disk=save_to_disk,
|
||
image_format=image_format,
|
||
)
|
||
)
|
||
tasks.append(task)
|
||
|
||
batch_results = []
|
||
for coro in asyncio.as_completed(tasks):
|
||
result_data = None
|
||
try:
|
||
result = await coro
|
||
if isinstance(result, Exception):
|
||
result_data = {"success": False, "error": str(result), "generated_count": 0, "saved_files": [], "prompt": ""}
|
||
else:
|
||
result_data = result
|
||
batch_results.append(result_data)
|
||
except Exception as e:
|
||
result_data = {"success": False, "error": str(e), "generated_count": 0, "saved_files": [], "prompt": ""}
|
||
batch_results.append(result_data)
|
||
|
||
completed += 1
|
||
prompt_snippet = (result_data.get("prompt", "") or "")[:30]
|
||
|
||
if result_data and result_data.get("success", False):
|
||
success_count += 1
|
||
count = result_data.get("generated_count", 1)
|
||
print(f"Nano Banana Pro: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✓成功({count}张)")
|
||
else:
|
||
fail_count += 1
|
||
error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
|
||
print(f"Nano Banana Pro: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✗失败: {error_msg}")
|
||
|
||
if pbar is not None:
|
||
pbar.update(1)
|
||
|
||
all_results.extend(batch_results)
|
||
|
||
import gc
|
||
gc.collect()
|
||
|
||
await asyncio.sleep(0.1)
|
||
|
||
return all_results
|
||
|
||
def generate(
|
||
self,
|
||
prompt: str,
|
||
模型: str,
|
||
宽高比: str,
|
||
分辨率: str,
|
||
生图数量: int,
|
||
seed: int,
|
||
**kwargs
|
||
) -> Tuple[torch.Tensor]:
|
||
"""
|
||
生成图像
|
||
|
||
Args:
|
||
prompt: 提示词
|
||
模型: 模型名称
|
||
宽高比: 宽高比
|
||
分辨率: 分辨率
|
||
生图数量: 批次大小
|
||
seed: 随机种子
|
||
**kwargs: 搜索开关（谷歌搜索（联网）/ 图片搜索（联网））及动态参考图输入 (参考图1-9)
|
||
注：两个搜索参数名含全角括号，不能作为 Python 形参，从 kwargs 中提取
|
||
|
||
注意：
|
||
调试日志功能已移至文件顶部配置，通过修改 DEBUG_LOG_ENABLED 常量控制
|
||
|
||
Returns:
|
||
生成的图像张量 (IMAGE,)
|
||
"""
|
||
start_time = time.time()
|
||
|
||
# 从 kwargs 提取搜索参数（界面显示为「关闭/打开」，转为 bool 供调用）
|
||
enable_grounding: bool = (kwargs.pop("谷歌搜索（联网）", "关闭") == "打开")
|
||
enable_image_search: bool = (kwargs.pop("图片搜索（联网）", "关闭") == "打开")
|
||
proxy_port: str = kwargs.pop("代理端口（如7897）", "")
|
||
image_format: str = kwargs.pop("返回格式", "url")
|
||
|
||
# 创建 ComfyUI 原生进度条
|
||
pbar = None
|
||
if PROGRESS_BAR_AVAILABLE:
|
||
pbar = ProgressBar(生图数量)
|
||
|
||
try:
|
||
# 设置随机种子（用于本地随机操作）
|
||
random.seed(seed)
|
||
np.random.seed(seed % (2**32))
|
||
|
||
# 内存监控初始化
|
||
if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
|
||
import psutil
|
||
process = psutil.Process()
|
||
initial_memory = process.memory_info().rss / 1024 / 1024
|
||
print(f"Nano Banana Pro: 初始内存使用: {initial_memory:.1f} MB")
|
||
|
||
# 初始化 API 客户端
|
||
if self.client is None:
|
||
try:
|
||
self.client = GeminiAPIClient()
|
||
except ValueError as e:
|
||
raise ValueError(f"初始化失败: {str(e)}")
|
||
|
||
# 注入代理设置（每次执行都刷新，支持用户中途修改端口）
|
||
self.client.proxy_url = GeminiAPIClient.build_proxy_url(proxy_port)
|
||
if self.client.proxy_url:
|
||
print(f"Nano Banana Pro: 已启用代理加速 → {self.client.proxy_url}")
|
||
|
||
# 校验分辨率与模型的兼容性
|
||
supported_resolutions = get_model_supported_resolutions(模型)
|
||
if supported_resolutions and 分辨率 not in supported_resolutions:
|
||
raise ValueError(
|
||
f"分辨率 \"{分辨率}\" 与模型 \"{模型}\" 不兼容！\n"
|
||
f"该模型支持的分辨率：{', '.join(supported_resolutions)}"
|
||
)
|
||
|
||
# 校验宽高比与模型的兼容性
|
||
supported_ratios = get_model_supported_aspect_ratios(模型)
|
||
if supported_ratios and 宽高比 not in supported_ratios:
|
||
raise ValueError(
|
||
f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
|
||
f"该模型支持的宽高比：{', '.join(supported_ratios)}"
|
||
)
|
||
|
||
# 校验图片搜索（联网）与模型的兼容性
|
||
# 仅 nano-banana-2-限时特价 和 gemini-3.1-flash-image-preview 支持图片搜索
|
||
IMAGE_SEARCH_UNSUPPORTED_MODELS = ["nano-banana-pro-次卡", "nano-banana-pro-官方计费", "gemini-3-pro-image-preview"]
|
||
if enable_image_search and 模型 in IMAGE_SEARCH_UNSUPPORTED_MODELS:
|
||
raise ValueError(
|
||
f"模型 \"{模型}\" 不支持【图片搜索（联网）】功能！"
|
||
f"请切换到 nano-banana-2-限时特价 或 gemini-3.1-flash-image-preview 后再使用"
|
||
)
|
||
|
||
# 收集独立输入的参考图
|
||
input_images = []
|
||
for i in range(1, 10): # 1-9
|
||
key = f"参考图{i}"
|
||
if key in kwargs and kwargs[key] is not None:
|
||
pil_imgs = tensor_to_pil(kwargs[key])
|
||
input_images.extend(pil_imgs)
|
||
|
||
# 验证输入图像数量
|
||
if input_images:
|
||
if len(input_images) > 14:
|
||
raise ValueError(
|
||
f"输入图像数量 {len(input_images)} 超过限制 14 张，请减少输入图像数量"
|
||
)
|
||
|
||
# 解析批量提示词
|
||
batch_prompts = parse_batch_prompts(prompt)
|
||
|
||
# 打印首行概览
|
||
# 图片搜索（联网）开启时隐含谷歌搜索接地，与客户端请求逻辑保持一致
|
||
grounding_str = ""
|
||
if enable_image_search:
|
||
grounding_str = " | 谷歌图片搜索接地"
|
||
elif enable_grounding:
|
||
grounding_str = " | 谷歌搜索接地"
|
||
|
||
if batch_prompts:
|
||
# 批量提示词模式
|
||
num_prompts = len(batch_prompts)
|
||
total_images = num_prompts * 生图数量
|
||
mode_str = f"批量提示词模式 ({num_prompts}个提示词)"
|
||
if input_images:
|
||
mode_str += f" (输入{len(input_images)}张)"
|
||
print(f"Nano Banana Pro: {mode_str} | {分辨率} {宽高比} | 共{total_images}张{grounding_str}")
|
||
|
||
# 大批量警告
|
||
if total_images > 100:
|
||
print(f"⚠️ Nano Banana Pro: 警告！批量生成 {total_images} 张图片，内存占用可能较高")
|
||
print(f"⚠️ 建议：分批执行或减少生图数量")
|
||
else:
|
||
# 单提示词模式
|
||
mode_str = f"图生图模式 (输入{len(input_images)}张)" if input_images else "文生图模式"
|
||
print(f"Nano Banana Pro: {mode_str} | {分辨率} {宽高比} | {生图数量}张{grounding_str}")
|
||
|
||
# 大批量警告
|
||
if 生图数量 > 100:
|
||
print(f"⚠️ Nano Banana Pro: 警告！批量生成 {生图数量} 张图片，内存占用可能较高")
|
||
print(f"⚠️ 建议：分批执行或减少生图数量")
|
||
|
||
# 统计变量
|
||
success_count = 0
|
||
fail_count = 0
|
||
|
||
# 进度回调 - 打印错误信息并更新进度条，添加内存监控
|
||
def progress_callback(current, total, success, error_msg=None):
|
||
nonlocal success_count, fail_count
|
||
if success:
|
||
success_count += 1
|
||
else:
|
||
fail_count += 1
|
||
|
||
# 更新 ComfyUI 原生进度条
|
||
if pbar is not None:
|
||
pbar.update(1)
|
||
|
||
# 内存监控（每完成10个任务检查一次）
|
||
if MEMORY_MONITOR_AVAILABLE and total > 50 and current % 10 == 0:
|
||
import gc
|
||
gc.collect() # 强制垃圾回收
|
||
current_memory = process.memory_info().rss / 1024 / 1024
|
||
memory_increase = current_memory - initial_memory
|
||
print(f"Nano Banana Pro: 内存使用: {current_memory:.1f} MB (+{memory_increase:.1f} MB)")
|
||
|
||
# 内存警告阈值（2GB）
|
||
if current_memory > 2000:
|
||
print(f"⚠️ Nano Banana Pro: 内存使用过高！建议减少生图数量或分批执行")
|
||
|
||
# 根据是否有批量提示词选择生成模式
|
||
if batch_prompts:
|
||
num_prompts = len(batch_prompts)
|
||
total_images = num_prompts * 生图数量
|
||
|
||
# ===== 批量提示词模式：异步并发，内存输出 =====
|
||
if pbar is not None:
|
||
pbar = ProgressBar(total_images)
|
||
|
||
def run_async_in_thread():
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
return loop.run_until_complete(
|
||
self._process_batch_async(
|
||
prompts=batch_prompts,
|
||
model=模型,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
images_per_prompt=生图数量,
|
||
input_images=input_images,
|
||
output_folder="",
|
||
pbar=pbar,
|
||
enable_grounding=enable_grounding,
|
||
enable_image_search=enable_image_search,
|
||
save_to_disk=False,
|
||
image_format=image_format,
|
||
)
|
||
)
|
||
finally:
|
||
loop.close()
|
||
|
||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||
future = executor.submit(run_async_in_thread)
|
||
try:
|
||
results = future.result(timeout=900)
|
||
except TimeoutError:
|
||
raise RuntimeError("任务执行超时（900秒），请减少提示词数量或检查网络连接")
|
||
|
||
# 统计结果
|
||
success_count = sum(1 for r in results if r.get("success", False))
|
||
fail_count = len(results) - success_count
|
||
total_generated = sum(r.get("generated_count", 0) for r in results)
|
||
|
||
elapsed = time.time() - start_time
|
||
time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
|
||
|
||
print(f"完成！总耗时 {time_str} | 成功: {success_count}/{total_images} | 失败: {fail_count}")
|
||
|
||
# 失败详情
|
||
failed_results = [r for r in results if not r.get("success", False)]
|
||
if failed_results:
|
||
for fr in failed_results:
|
||
idx = fr.get("global_task_index", -1) + 1
|
||
prompt_snippet = (fr.get("prompt", "") or "")[:30]
|
||
error_msg = fr.get("error", "未知错误")
|
||
print(f" 失败 #{idx}: {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → {error_msg}")
|
||
|
||
# 收集内存中的图像
|
||
output_images = []
|
||
for r in results:
|
||
output_images.extend(r.get("output_images", []))
|
||
|
||
if not output_images:
|
||
placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
|
||
output_images = [placeholder]
|
||
|
||
output_tensor = _images_to_tensor_safe(output_images, _NODE)
|
||
|
||
import gc
|
||
gc.collect()
|
||
return (output_tensor,)
|
||
else:
|
||
# 单提示词模式
|
||
if 生图数量 == 1:
|
||
# 单张：同步生成
|
||
generated_images = self.client.generate_sync(
|
||
prompt=prompt,
|
||
model=模型,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
batch_size=1,
|
||
images=input_images,
|
||
progress_callback=progress_callback,
|
||
debug=DEBUG_LOG_ENABLED,
|
||
debug_request=REQUEST_LOG_ENABLED,
|
||
enable_grounding=enable_grounding,
|
||
enable_image_search=enable_image_search,
|
||
image_format=image_format,
|
||
)
|
||
else:
|
||
# 多张：异步并发，内存输出
|
||
|
||
if pbar is not None:
|
||
pbar = ProgressBar(生图数量)
|
||
|
||
def run_async_in_thread():
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
return loop.run_until_complete(
|
||
self._process_batch_async(
|
||
prompts=[prompt],
|
||
model=模型,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
images_per_prompt=生图数量,
|
||
input_images=input_images,
|
||
output_folder="",
|
||
pbar=pbar,
|
||
enable_grounding=enable_grounding,
|
||
enable_image_search=enable_image_search,
|
||
save_to_disk=False,
|
||
image_format=image_format,
|
||
)
|
||
)
|
||
finally:
|
||
loop.close()
|
||
|
||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||
future = executor.submit(run_async_in_thread)
|
||
try:
|
||
results = future.result(timeout=900)
|
||
except TimeoutError:
|
||
raise RuntimeError("任务执行超时（900秒），请减少生图数量或检查网络连接")
|
||
|
||
success_count = sum(1 for r in results if r.get("success", False))
|
||
fail_count = len(results) - success_count
|
||
total_generated = sum(r.get("generated_count", 0) for r in results)
|
||
|
||
elapsed = time.time() - start_time
|
||
time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
|
||
print(f"完成！总耗时 {time_str} | 成功: {success_count}/{生图数量} | 失败: {fail_count}")
|
||
|
||
# 失败详情
|
||
failed_results = [r for r in results if not r.get("success", False)]
|
||
if failed_results:
|
||
for fr in failed_results:
|
||
idx = fr.get("global_task_index", -1) + 1
|
||
error_msg = fr.get("error", "未知错误")
|
||
print(f" 失败 #{idx}: {prompt[:30]}{'...' if len(prompt) >= 30 else ''} → {error_msg}")
|
||
|
||
# 收集内存中的图像
|
||
output_images = []
|
||
for r in results:
|
||
output_images.extend(r.get("output_images", []))
|
||
|
||
if not output_images:
|
||
placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
|
||
output_images = [placeholder]
|
||
|
||
output_tensor = _images_to_tensor_safe(output_images, _NODE)
|
||
|
||
import gc
|
||
gc.collect()
|
||
return (output_tensor,)
|
||
|
||
|
||
# 优化：限制输出图片数量，避免内存爆炸
|
||
max_output_images = 20 # 最多输出20张图片到ComfyUI
|
||
|
||
if len(generated_images) > max_output_images:
|
||
print(f"Nano Banana Pro: 生成 {len(generated_images)} 张图片，限制输出前 {max_output_images} 张到ComfyUI")
|
||
output_images = generated_images[:max_output_images]
|
||
else:
|
||
output_images = generated_images
|
||
|
||
# 转换输出图像
|
||
output_tensor = _images_to_tensor_safe(output_images, _NODE)
|
||
|
||
# 计算耗时并打印最终统计
|
||
elapsed = time.time() - start_time
|
||
if elapsed < 1:
|
||
time_str = f"{elapsed:.3f}s"
|
||
else:
|
||
time_str = f"{elapsed:.2f}s"
|
||
|
||
# 打印最终汇总
|
||
if fail_count > 0:
|
||
print(f"完成！总耗时 {time_str} | 成功 {success_count}张 | 失败 {fail_count}张")
|
||
else:
|
||
print(f"完成！总耗时 {time_str} | 成功 {len(generated_images)}张")
|
||
|
||
# 最终内存清理
|
||
import gc
|
||
gc.collect()
|
||
if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
|
||
final_memory = process.memory_info().rss / 1024 / 1024
|
||
print(f"Nano Banana Pro: 最终内存使用: {final_memory:.1f} MB")
|
||
|
||
return (output_tensor,)
|
||
|
||
except ValueError as e:
|
||
# 检测是否为授权错误
|
||
if str(e) == "未授权！":
|
||
print("请联系作者授权后方可使用！")
|
||
raise ValueError("未授权！") from None
|
||
raise ValueError(str(e)) from None
|
||
|
||
except RuntimeError as e:
|
||
raise RuntimeError(str(e)) from None
|
||
|
||
except Exception as e:
|
||
raise type(e)(str(e)) from None
|
||
|
||
finally:
|
||
# 查询余额
|
||
if self.client is not None:
|
||
try:
|
||
balance_data = self.client.query_balance_sync()
|
||
balance_info = self.client.format_balance_info(balance_data)
|
||
print(f"Nano Banana Pro: {balance_info}")
|
||
except Exception:
|
||
pass
|
||
|
||
# 最终内存清理
|
||
import gc
|
||
gc.collect() |